NeTS: Medium: Collaborative Research: Shaping, Learning and Optimizing Dynamic Networks

NeTS：媒介：协作研究：塑造、学习和优化动态网络

• 批准号: 0963989
• 负责人: Sekhar Tatikonda
• 金额: $ 25万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-04-01 至 2015-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0963989&HistoricalAwards=false
• 关键词: NeTS; Medium; Collaborative; Research; Shaping

This project develops, from the ground up, a new theoretical framework for analyzing and designing algorithms for dynamic ad-hoc wireless networks. This proposal embraces network dynamics as an opportunity to be exploited, not an adversity to be overcome. The approach is based on four inter-related thrusts:1. Incremental Topology Learning: Tracking changes in the network much more efficiently than re-learning entire topology, using sparse "error graph" representations.2. Topology and Traffic Shaping: Controlling the "effective" wireless network topology so that (i) at any instant of time it appears to be highly disconnected to scheduling algorithms, but retains global connectivity over time; and (ii) modifying traffic statistics to ensure statistical spatial correlation decay.3. Warm-starting Distributed Algorithms: Message-passing algorithms that can warm-start the optimization based on local knowledge of past solutions.4. Proteus - A Mobile Robot Testbed: This project validates its approach via implementation on a mobile robot testbed called Proteus, which is used to optimize algorithms in a practical setting.Broader Impact: Industry is involved in this research from the start, via the WNCG Affiliates program at UT. The research will be disseminated via publications in top-tier venues, industry interactions, and specially organized workshops. Both graduate students and undergraduate students, via a REU program at UT (with emphasis on recruiting women and minorities), get exposure to both real-world wireless networks (via the testbed), and cutting edge theory.

该项目从头开始开发了一个新的理论框架，用于分析和设计动态自组织无线网络的算法。该提案将网络动态视为一个可以利用的机会，而不是一个需要克服的逆境。该方法基于四个相互关联的主旨：1．增量拓扑学习：使用稀疏的“错误图”表示，跟踪网络中的变化比重新学习整个拓扑更有效。2。拓扑和流量整形：控制“有效”的无线网络拓扑，以便（i）在任何时刻它看起来与调度算法高度断开，但随着时间的推移保持全局连接； (ii) 修改流量统计以确保统计空间相关性衰减。3．热启动分布式算法：消息传递算法，可以根据过去解决方案的局部知识来热启动优化。4． Proteus - 移动机器人测试台：该项目通过在名为 Proteus 的移动机器人测试台上实施来验证其方法，该测试台用于在实际环境中优化算法。更广泛的影响：业界从一开始就通过 UT 的 WNCG 附属计划参与了这项研究。 该研究将通过顶级场所的出版物、行业互动和专门组织的研讨会进行传播。通过 UT 的 REU 项目（重点是招募女性和少数族裔），研究生和本科生都可以接触到现实世界的无线网络（通过测试平台）和前沿理论。